Embodiments of the present disclosure relate to solar energy, and more particularly to measurement, calculation, and prediction of cloud characteristics such as cloud movement.
Images of the sky, commonly referred to as ‘sky images,’ are employed in diverse applications. For example, astronomers utilize sky images for analyzing cosmic movements and meteorological departments use these images to predict local weather conditions. Similarly, in one application, sky images may be utilized to observe the sun and clouds and to predict cloud cover, cloud movement over a given period, and solar radiation received at a particular location over a given time period. Determination of such cloud characteristics and solar irradiance may be employed in solar plants (employing solar panels or photovoltaic power grids) to ascertain or predict power output of the grid for particular time intervals and to control the grid output based on cloud movement. For example, when a cloud's shadow falls on the solar panels, the shadow may partially or completely block sunlight, thereby limiting the power output of the solar panels. During this time, the solar plant may need to operate auxiliary energy sources, such as batteries or thermal power plants to maintain the power output of the solar plant. Once the cloud cover drifts away, the auxiliary power source may be disconnected, and power may once again be provided by the solar panels.
The prediction of cloud movement, however, is technically challenging. By way of example, since the sky typically spans over a wide angle of view, the movement of distant low clouds may not be easily detected. For instance, it may be difficult to calculate the velocity, size, and orientation of clouds near the horizon. Conventional sky-imaging devices typically utilize a combination of standard digital camera sensors with wide-angle lenses such as conventional fish-eye lenses or hemispherical mirrors to capture a large part of the visible sky. These wide-angle lenses, however, fail to accurately detect cloud movement of low-lying and fast-moving clouds at the circumference of the lens and therefore may not be able to effectively capture cloud movement. In particular, the images captured by these lenses are compressed near the circumference of the lens (i.e., a large portion of the sky near the horizon is mapped to a relatively small part of the camera sensor) and expanded near the center of the lens (i.e., a small portion of the sky is mapped to a relatively larger part of the camera sensor). Therefore, in an image, a large cloud at the horizon may appear relatively smaller than a smaller cloud present at the center of the lens. Moreover, a relatively larger movement of higher velocity clouds at the circumference of the lens may appear smaller than a relatively smaller movement of lower velocity clouds at the center of the lens.
A large prediction horizon is a desirable feature of a prediction system as that allows corrective actions to be initiated as early as possible. Prediction time refers to the amount of time in which a prediction system is able to determine an occlusion event (i.e., when a cloud cover shades the solar panels) before the event occurs. So, in order to predict an occlusion event as early as possible, it is desirable that the prediction system detect clouds as early as possible. To this end, it is desirable that the lens detects clouds and determines their characteristics when the clouds are as far away from the solar plant as possible. Timely calculations are desired because these calculations may be utilized for further computations and/or actions. For example, based on the computed cloud characteristics, the prediction system may be configured to estimate the solar irradiance received by the solar panels and the electric power output of the solar panels during an occlusion event. Additionally, based on the power output so computed, the prediction system may be configured to determine whether there is a need for auxiliary power sources. As will be appreciated, certain auxiliary sources, such as thermal power sources may reach a steady state in a determined time subsequent to being powered on. If these auxiliary sources do not reach steady state before the occlusion event, the power output of the solar plant may disadvantageously fall below a threshold value. In a conventional fish-eye lens, because of the compressive property of the lens, the prediction horizon is small. Consequently, cloud movement and other characteristics are not easily discernible when the cloud is near the horizon. Waiting for the cloud to reach the center of the lens, where the cloud movement and other characteristics are easily discernible, results in a delay in the computation of power output. Accordingly, there may be a delay in energizing an auxiliary source and allowing the auxiliary source to reach steady state, thereby reducing the power output of the solar plant.